Load dataset: This has already had exclusions applied to it, it is a complete case dataset
Data set of complete case participants for analysis
Create Table 1 (Demographics)
Age has already been limited to age 35-65
Ethnicity has multiple catagoies in UK Biobank, these have been recoded to groupings that are most relevant to bone health.
Ethnicity was recoded to show the following groupings - 1 = White, - 2 = Asian and British Asian, - 3 = Black and Black British, - 4 = Mixed
Participants were asked about activities undertaken in the last 4 weeks including frequency, intensity and duration. These are reported as raw values. From these a number of derived Metabolic Equivalent Task (MET) minutes of activity were also derived.
We have added a derived value of mins/wk spent in each activity for use in comparing to the accelerometry data later.
Activity measures (split into moderate, vigorous, walking and summed activity)
We are using only participants with all of duration and number of days/wk of activity in our main analysis. Those with missing values in either column are considered NA in the MET calculations and mins/wk calculations. This differs to the UKB anaysis where some data is imputed if just duration is missing. We will use this for comparison in the sensitivity analysis.
The full IPAQ calculates total physical activity as a combination of both leisure time PA (LTPA) as well as domains of work and normal activity - as such the resulting median MET-minutes is higher than for participation in LTPA alone. The general public health recommendations of 30 mins of MVPA are relatively low with most adults being able to achieve this regardless of leisure time activity. To look at the health benefits of LTPA higher cut point thresholds are needed for UKB IPAQ data.
## Saved: ../results/tables/Table_3.1.docx
sex | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
Female | mins_wk_mod | 171,043 | 248.06 | 404.29 | 120 | 0 | 8,820 | 30 | 300 |
Female | MET_mod | 171,043 | 992.22 | 1,617.15 | 480 | 0 | 35,280 | 120 | 1,200 |
Male | mins_wk_mod | 151,710 | 281.74 | 493.26 | 105 | 0 | 10,080 | 30 | 300 |
Male | MET_mod | 151,710 | 1,126.95 | 1,973.04 | 420 | 0 | 40,320 | 120 | 1,200 |
Total | mins_wk_mod | 322,753 | 263.89 | 448.63 | 120 | 0 | 10,080 | 30 | 300 |
Total | MET_mod | 322,753 | 1,055.55 | 1,794.51 | 480 | 0 | 40,320 | 120 | 1,200 |
## Saved: ../results/tables/Table_3.2.docx
sex | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
Female | mins_wk_vig | 171,043 | 75.97 | 149.25 | 24 | 0 | 8,400 | 0 | 90 |
Female | MET_vig | 171,043 | 607.75 | 1,194.03 | 192 | 0 | 67,200 | 0 | 720 |
Male | mins_wk_vig | 151,710 | 112.74 | 240.98 | 40 | 0 | 7,560 | 0 | 125 |
Male | MET_vig | 151,710 | 901.91 | 1,927.85 | 320 | 0 | 60,480 | 0 | 1,000 |
Total | mins_wk_vig | 322,753 | 93.25 | 198.59 | 30 | 0 | 8,400 | 0 | 120 |
Total | MET_vig | 322,753 | 746.02 | 1,588.74 | 240 | 0 | 67,200 | 0 | 960 |
## Saved: ../results/tables/Table_3.3.docx
sex | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
Female | mins_wk_walk | 171,043 | 359.38 | 505.59 | 210 | 0 | 10,080 | 100 | 420 |
Female | MET_walk | 171,043 | 1,185.94 | 1,668.45 | 693 | 0 | 33,264 | 330 | 1,386 |
Male | mins_wk_walk | 151,710 | 381.53 | 567.80 | 210 | 0 | 8,750 | 90 | 420 |
Male | MET_walk | 151,710 | 1,259.05 | 1,873.73 | 693 | 0 | 28,875 | 297 | 1,386 |
Total | mins_wk_walk | 322,753 | 369.79 | 535.84 | 210 | 0 | 10,080 | 90 | 420 |
Total | MET_walk | 322,753 | 1,220.31 | 1,768.29 | 693 | 0 | 33,264 | 297 | 1,386 |
## Saved: ../results/tables/Table_3.4.docx
sex | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
Female | mins_wk_MVPA | 171,043 | 324.02 | 481.23 | 175 | 0 | 10,920 | 60 | 400 |
Female | MET_MVPA | 171,043 | 1,599.97 | 2,346.96 | 840 | 0 | 77,280 | 240 | 2,000 |
Male | mins_wk_MVPA | 151,710 | 394.47 | 643.13 | 180 | 0 | 15,120 | 50 | 440 |
Male | MET_MVPA | 151,710 | 2,028.85 | 3,346.82 | 960 | 0 | 90,720 | 240 | 2,400 |
Total | mins_wk_MVPA | 322,753 | 357.14 | 564.25 | 180 | 0 | 15,120 | 55 | 420 |
Total | MET_MVPA | 322,753 | 1,801.57 | 2,868.80 | 920 | 0 | 90,720 | 240 | 2,160 |
Sum of days performing walking, moderate and vigorous activity - question derived from answers to number of days doing each of the following in a week so total of all will add up to more than 7 but cant be over 21.
As with the above this is the total amount of time in minutes spent walking, doing vigorous and doing moderate PA. Unlike summed days this should not exceed the total number of minutes in a week
## Saved: ../results/tables/Table_3.3.docx
sex | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
Female | summed_MET_all | 171,043 | 2,785.92 | 3,398.64 | 1,734 | 0 | 80,052 | 812 | 3,408 |
Male | summed_MET_all | 151,710 | 3,287.90 | 4,585.45 | 1,824 | 0 | 115,668 | 813 | 3,786 |
Total | summed_MET_all | 322,753 | 3,021.88 | 4,008.42 | 1,773 | 0 | 115,668 | 813 | 3,573 |
Based on activity frequency, duration and intensity an activity group is derived as being low, moderate or high active.
## Saved: ../results/tables/Table_3.5.docx
Fracture status | Variable | N | Mean | SD | Median | Min | Max | IQR_low | IQR_high |
|---|---|---|---|---|---|---|---|---|---|
No | MET_mod | 292,665 | 1,040.97 | 1,776.11 | 420 | 0 | 40,320 | 120 | 1,200 |
No | MET_vig | 292,665 | 732.44 | 1,565.01 | 240 | 0 | 67,200 | 0 | 960 |
No | MET_MVPA | 292,665 | 1,773.41 | 2,830.07 | 900 | 0 | 80,640 | 240 | 2,160 |
No | MET_walk | 292,665 | 1,209.39 | 1,751.58 | 693 | 0 | 33,264 | 297 | 1,386 |
No | |||||||||
Yes | MET_mod | 30,088 | 1,197.37 | 1,958.92 | 480 | 0 | 30,240 | 160 | 1,440 |
Yes | MET_vig | 30,088 | 878.11 | 1,798.05 | 320 | 0 | 60,480 | 0 | 1,080 |
Yes | MET_MVPA | 30,088 | 2,075.48 | 3,208.53 | 1,080 | 0 | 90,720 | 280 | 2,520 |
Yes | MET_walk | 30,088 | 1,326.53 | 1,919.99 | 693 | 0 | 28,875 | 330 | 1,386 |
Yes | |||||||||
MET_mod | 322,753 | 1,055.55 | 1,794.51 | 480 | 0 | 40,320 | 120 | 1,200 | |
MET_vig | 322,753 | 746.02 | 1,588.74 | 240 | 0 | 67,200 | 0 | 960 | |
MET_MVPA | 322,753 | 1,801.57 | 2,868.80 | 920 | 0 | 90,720 | 240 | 2,160 | |
MET_walk | 322,753 | 1,220.31 | 1,768.29 | 693 | 0 | 33,264 | 297 | 1,386 | |
#### MVPA
All data is skewed, look at whether log transformation helps normalise the data
## # A tibble: 4 × 4
## MET_mod_bin N Fractures `Fracture %`
## <fct> <int> <int> <dbl>
## 1 0–150 min 86203 7376 8.56
## 2 151–300 min 46941 4070 8.67
## 3 301–450 min 27243 2359 8.66
## 4 >450 min 159393 15911 9.98
## Warning: Use of .data in tidyselect expressions was deprecated in tidyselect 1.2.0.
## ℹ Please use `all_of(var)` (or `any_of(var)`) instead of `.data[[var]]`
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## # A tibble: 16 × 5
## PA_variable Bin N Fractures `Fracture %`
## <chr> <fct> <int> <int> <dbl>
## 1 MET_MVPA 0–150 min 63357 5445 8.59
## 2 MET_MVPA 151–300 min 26798 2261 8.44
## 3 MET_MVPA 301–450 min 19322 1650 8.54
## 4 MET_MVPA >450 min 210303 20360 9.68
## 5 MET_mod 0–150 min 86203 7376 8.56
## 6 MET_mod 151–300 min 46941 4070 8.67
## 7 MET_mod 301–450 min 27243 2359 8.66
## 8 MET_mod >450 min 159393 15911 9.98
## 9 MET_vig 0–150 min 138423 12146 8.77
## 10 MET_vig 151–300 min 31411 2808 8.94
## 11 MET_vig 301–450 min 16863 1502 8.91
## 12 MET_vig >450 min 133083 13260 9.96
## 13 MET_walk 0–150 min 41212 3644 8.84
## 14 MET_walk 151–300 min 41319 3555 8.6
## 15 MET_walk 301–450 min 32474 2834 8.73
## 16 MET_walk >450 min 204775 19683 9.61
#### Extreme behaviour
## # A tibble: 12 × 5
## Bin N Fractures `Fracture %` PA_variable
## <fct> <int> <int> <dbl> <chr>
## 1 Bottom 10% 39733 3499 8.81 MET_MVPA
## 2 Middle 80% 248214 22545 9.08 MET_MVPA
## 3 Top 10% 31833 3672 11.5 MET_MVPA
## 4 Bottom 10% 46092 4026 8.73 MET_mod
## 5 Middle 80% 243519 22408 9.2 MET_mod
## 6 Top 10% 30169 3282 10.9 MET_mod
## 7 Bottom 10% 118157 10397 8.8 MET_vig
## 8 Middle 80% 175003 16116 9.21 MET_vig
## 9 Top 10% 26620 3203 12.0 MET_vig
## 10 Bottom 10% 36806 3278 8.91 MET_walk
## 11 Middle 80% 257881 23791 9.23 MET_walk
## 12 Top 10% 25093 2647 10.6 MET_walk
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## # A tibble: 36 × 6
## # Groups: agegp_A0 [3]
## agegp_A0 Bin N Fractures `Fracture %` PA_variable
## <fct> <fct> <int> <int> <dbl> <chr>
## 1 40-49 Bottom 10% 10729 828 7.72 MET_MVPA
## 2 40-49 Middle 80% 71137 6410 9.01 MET_MVPA
## 3 40-49 Top 10% 8590 1149 13.4 MET_MVPA
## 4 50-59 Bottom 10% 16825 1504 8.94 MET_MVPA
## 5 50-59 Middle 80% 96403 8538 8.86 MET_MVPA
## 6 50-59 Top 10% 11391 1251 11.0 MET_MVPA
## 7 60-69 Bottom 10% 12179 1167 9.58 MET_MVPA
## 8 60-69 Middle 80% 80674 7597 9.42 MET_MVPA
## 9 60-69 Top 10% 11852 1272 10.7 MET_MVPA
## 10 40-49 Bottom 10% 13247 1048 7.91 MET_mod
## # ℹ 26 more rows
#### age and sex
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## # A tibble: 72 × 7
## # Groups: agegp_A0, sex [6]
## agegp_A0 sex Bin N Fractures `Fracture %` PA_variable
## <fct> <fct> <fct> <int> <int> <dbl> <chr>
## 1 40-49 Female Bottom 10% 6123 404 6.6 MET_MVPA
## 2 40-49 Female Middle 80% 39239 2789 7.11 MET_MVPA
## 3 40-49 Female Top 10% 3621 374 10.3 MET_MVPA
## 4 40-49 Male Bottom 10% 4606 424 9.21 MET_MVPA
## 5 40-49 Male Middle 80% 31898 3621 11.4 MET_MVPA
## 6 40-49 Male Top 10% 4969 775 15.6 MET_MVPA
## 7 50-59 Female Bottom 10% 9220 897 9.73 MET_MVPA
## 8 50-59 Female Middle 80% 53376 4905 9.19 MET_MVPA
## 9 50-59 Female Top 10% 5164 560 10.8 MET_MVPA
## 10 50-59 Male Bottom 10% 7605 607 7.98 MET_MVPA
## # ℹ 62 more rows